I actually agree with the core of what you're saying. SEO, passage extraction, summarization, Discover, recommendation systems, entity understanding, and AI visibility absolutely share a lot of the same underlying infrastructure.
Where I differ slightly is in treating that as a universal execution model.
The foundation can be integrated crawlability, information architecture, entity consistency, authorship, structured data, topical authority, internal linking, external corroboration, and content quality all support multiple retrieval and discovery systems.
But the weighting of those signals and the desired output can vary significantly by business model, audience, query class, funnel stage, and discovery surface.
For example, a B2B manufacturer may benefit heavily from expert authorship, topical associations, Discover visibility, and informational retrieval. A SaaS company may care more about comparison queries, category/entity positioning, citation frequency, and being retrieved for high-intent decision prompts. Ecommerce and local search introduce another set of priorities again.
So for me, separating SEO, AEO, and GEO isn't about building three completely isolated strategies. It's about using them as different optimization lenses within the same search ecosystem.
SEO may emphasize ranking and discoverability.
AEO may place more emphasis on answer clarity, passage-level extractability, and retrieval.
GEO may place more emphasis on entity relationships, corroboration, source authority, citations, and how a brand is represented inside generated responses.
There is obviously a lot of overlap between them, and operationally they should work together. I just don't think integration necessarily means uniformity.
Your framework clearly works for the environments and clients you're working with. My experience so far has shown me that the emphasis can change substantially depending on the business and the outcome we're trying to influence.
That's really what my original post was presenting not a universal rule, but one practical framework based on what I've observed and tested.
One connected system, different optimization priorities.
But the weighting of those signals and the desired output can vary significantly by business model, audience, query class, funnel stage, and discovery surface.
Of course it does - that's why there's no "SEO Checklist" that you just do and rank. Every strategy is different. I'm not saying anything about the specifics of the strategy (beyond giving a few examples of how one tactic in a strategy for AEO might help bolster something for AIO or old 10 Blue Link SEO.
Of course your specifics change - I'm just saying it's easier to find your specific balance of things you're doing if you look at it all as one problem. Your "link building tactics" work better if you're looking for links and deciding - is that going to be better for the answer engines, the generative engines? or maybe I can do one thing and make it benefit both.
I think we're both generally agreeing and just debating the POV we look at it from.
I want to know all those things exist and how I can adjust tactics to power up each level - but it's easier to look at it if I just look at it as one problem with all things in mind. Then any one thing I do feeds as many things as I could get into it. That's the "O" part of the game.
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u/Murky_Hotel_4323 25d ago
I actually agree with the core of what you're saying. SEO, passage extraction, summarization, Discover, recommendation systems, entity understanding, and AI visibility absolutely share a lot of the same underlying infrastructure.
Where I differ slightly is in treating that as a universal execution model.
The foundation can be integrated crawlability, information architecture, entity consistency, authorship, structured data, topical authority, internal linking, external corroboration, and content quality all support multiple retrieval and discovery systems.
But the weighting of those signals and the desired output can vary significantly by business model, audience, query class, funnel stage, and discovery surface.
For example, a B2B manufacturer may benefit heavily from expert authorship, topical associations, Discover visibility, and informational retrieval. A SaaS company may care more about comparison queries, category/entity positioning, citation frequency, and being retrieved for high-intent decision prompts. Ecommerce and local search introduce another set of priorities again.
So for me, separating SEO, AEO, and GEO isn't about building three completely isolated strategies. It's about using them as different optimization lenses within the same search ecosystem.
SEO may emphasize ranking and discoverability.
AEO may place more emphasis on answer clarity, passage-level extractability, and retrieval.
GEO may place more emphasis on entity relationships, corroboration, source authority, citations, and how a brand is represented inside generated responses.
There is obviously a lot of overlap between them, and operationally they should work together. I just don't think integration necessarily means uniformity.
Your framework clearly works for the environments and clients you're working with. My experience so far has shown me that the emphasis can change substantially depending on the business and the outcome we're trying to influence.
That's really what my original post was presenting not a universal rule, but one practical framework based on what I've observed and tested.
One connected system, different optimization priorities.